



'''simple docstring'''






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCAmelCase:   Optional[Any]					=		8
def  lowerCamelCase__			(		_A					,     _A=BITS							):
  a		:   Any	  =	x.device

  a		:   int	  =	(x * 255).int().clamp(0					,     255							)

  a		:   Any	  =	2 ** torch.arange(bits - 1					,     -1					,     -1					,     device=_A							)
  a		:   Optional[Any]	  =	rearrange(_A					,     'd -> d 1 1'							)
  a		:   int	  =	rearrange(_A					,     'b c h w -> b c 1 h w'							)

  a		:   Optional[int]	  =	((x & mask) != 0).float()
  a		:   Optional[int]	  =	rearrange(_A					,     'b c d h w -> b (c d) h w'							)
  a		:   int	  =	bits * 2 - 1
  return bits
def  lowerCamelCase__			(		_A					,     _A=BITS							):
  a		:   Optional[int]	  =	x.device

  a		:   Optional[Any]	  =	(x > 0).int()
  a		:   Union[str, Any]	  =	2 ** torch.arange(bits - 1					,     -1					,     -1					,     device=_A					,     dtype=torch.intaa							)

  a		:   str	  =	rearrange(_A					,     'd -> d 1 1'							)
  a		:   Optional[Any]	  =	rearrange(_A					,     'b (c d) h w -> b c d h w'					,     d=8							)
  a		:   Tuple	  =	reduce(x * mask					,     'b c d h w -> b c h w'					,     'sum'							)
  return (dec / 255).clamp(0.0					,     1.0							)
def  lowerCamelCase__			(		self					,     _A					,     _A					,     _A					,     _A = 0.0					,     _A = True					,     _A=None					,     _A = True					,     ):
  if self.num_inference_steps is None:
    raise ValueError(
        'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'							)

  # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  # Ideally, read DDIM paper in-detail understanding

  # Notation (<variable name> -> <name in paper>
  # - pred_noise_t -> e_theta(x_t, t)
  # - pred_original_sample -> f_theta(x_t, t) or x_0
  # - std_dev_t -> sigma_t
  # - eta -> η
  # - pred_sample_direction -> "direction pointing to x_t"
  # - pred_prev_sample -> "x_t-1"

  # 1. get previous step value (=t-1)
  a		:   str	  =	timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  a		:   Dict	  =	self.alphas_cumprod[timestep]
  a		:   Dict	  =	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  a		:   List[Any]	  =	1 - alpha_prod_t

  # 3. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  a		:   Dict	  =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  a		:   Any	  =	self.bit_scale
  if self.config.clip_sample:
    a		:   List[Any]	  =	torch.clamp(_A					,     -scale					,     _A							)

  # 5. compute variance: "sigma_t(η)" -> see formula (16)
  # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  a		:   Optional[int]	  =	self._get_variance(_A					,     _A							)
  a		:   Union[str, Any]	  =	eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    a		:   List[Any]	  =	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

  # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  a		:   List[Any]	  =	(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

  # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  a		:   Dict	  =	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

  if eta > 0:
    # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
    a		:   Union[str, Any]	  =	model_output.device if torch.is_tensor(_A							) else 'cpu'
    a		:   Dict	  =	torch.randn(model_output.shape					,     dtype=model_output.dtype					,     generator=_A							).to(_A							)
    a		:   Tuple	  =	self._get_variance(_A					,     _A							) ** 0.5 * eta * noise

    a		:   Optional[Any]	  =	prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_A					,     pred_original_sample=_A							)
def  lowerCamelCase__			(		self					,     _A					,     _A					,     _A					,     _A="epsilon"					,     _A=None					,     _A = True					,     ):
  a		:   Dict	  =	timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    a  ,      a		:   Dict	  =	torch.split(_A					,     sample.shape[1]					,     dim=1							)
  else:
    a		:   List[str]	  =	None

  # 1. compute alphas, betas
  a		:   Tuple	  =	self.alphas_cumprod[t]
  a		:   Any	  =	self.alphas_cumprod[t - 1] if t > 0 else self.one
  a		:   Union[str, Any]	  =	1 - alpha_prod_t
  a		:   Optional[Any]	  =	1 - alpha_prod_t_prev

  # 2. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  if prediction_type == "epsilon":
    a		:   Optional[Any]	  =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    a		:   Dict	  =	model_output
  else:
    raise ValueError(f"""Unsupported prediction_type {prediction_type}."""							)

  # 3. Clip "predicted x_0"
  a		:   List[str]	  =	self.bit_scale
  if self.config.clip_sample:
    a		:   int	  =	torch.clamp(_A					,     -scale					,     _A							)

  # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  a		:   Union[str, Any]	  =	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  a		:   Dict	  =	self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

  # 5. Compute predicted previous sample µ_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  a		:   int	  =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  a		:   List[Any]	  =	0
  if t > 0:
    a		:   int	  =	torch.randn(
        model_output.size()					,     dtype=model_output.dtype					,     layout=model_output.layout					,     generator=_A							).to(model_output.device							)
    a		:   Optional[int]	  =	(self._get_variance(_A					,     predicted_variance=_A							) ** 0.5) * noise

  a		:   Dict	  =	pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_A					,     pred_original_sample=_A							)

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Optional[int]		,     __snake_case					:    UNetaDConditionModel		,     __snake_case					:    Union[DDIMScheduler, DDPMScheduler]		,     __snake_case					:    Optional[float] = 1.0		,     ):
      super().__init__()
      a		:   List[Any]	  =	bit_scale
      a		:   Optional[Any]	  =	(
          ddim_bit_scheduler_step if isinstance(__snake_case		,     __snake_case      ) else ddpm_bit_scheduler_step
      )

      self.register_modules(unet=__snake_case		,     scheduler=__snake_case      )







    @torch.no_grad()
    def __call__( self					:    List[Any]		,     __snake_case					:    Optional[int] = 2_56		,     __snake_case					:    Optional[int] = 2_56		,     __snake_case					:    Optional[int] = 50		,     __snake_case					:    Optional[torch.Generator] = None		,     __snake_case					:    Optional[int] = 1		,     __snake_case					:    Optional[str] = "pil"		,     __snake_case					:    bool = True		,     **__snake_case					:    List[str]		,     ):
      a		:   Union[str, Any]	  =	torch.randn(
          (batch_size, self.unet.config.in_channels, height, width)		,     generator=__snake_case		,     )
      a		:   List[Any]	  =	decimal_to_bits(__snake_case      ) * self.bit_scale
      a		:   Tuple	  =	latents.to(self.device      )

      self.scheduler.set_timesteps(__snake_case      )

      for t in self.progress_bar(self.scheduler.timesteps      ):
        # predict the noise residual
        a		:   Union[str, Any]	  =	self.unet(__snake_case		,     __snake_case      ).sample

        # compute the previous noisy sample x_t -> x_t-1
        a		:   Any	  =	self.scheduler.step(__snake_case		,     __snake_case		,     __snake_case      ).prev_sample

      a		:   List[str]	  =	bits_to_decimal(__snake_case      )

      if output_type == "pil":
        a		:   Optional[Any]	  =	self.numpy_to_pil(__snake_case      )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__snake_case      )




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Optional[int]					=		logging.get_logger(__name__)

lowerCAmelCase:   int					=		{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """ctrl"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__( self					:    Optional[int]		,     __snake_case					:    Union[str, Any]=24_65_34		,     __snake_case					:    Dict=2_56		,     __snake_case					:    Optional[int]=12_80		,     __snake_case					:    Optional[int]=81_92		,     __snake_case					:    Union[str, Any]=48		,     __snake_case					:    str=16		,     __snake_case					:    Any=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    str=1e-6		,     __snake_case					:    List[str]=0.02		,     __snake_case					:    int=True		,     **__snake_case					:    Any		,     ):
      a		:   int	  =	vocab_size
      a		:   Union[str, Any]	  =	n_positions
      a		:   Optional[int]	  =	n_embd
      a		:   Dict	  =	n_layer
      a		:   Union[str, Any]	  =	n_head
      a		:   Tuple	  =	dff
      a		:   Union[str, Any]	  =	resid_pdrop
      a		:   Tuple	  =	embd_pdrop
      a		:   int	  =	layer_norm_epsilon
      a		:   str	  =	initializer_range

      a		:   Any	  =	use_cache

      super().__init__(**__snake_case      )